Calculation of digest segmentations for input data using similar data in a data deduplication system

ABSTRACT

For calculation of digest segmentations for input data using similar data in a data deduplication system using a processor device in a computing environment, a stream of input data is partitioned into input data chunks. Similar repository intervals are calculated for each input data chunk. Anchor positions are determined between an input data chunk and the similar repository intervals, based on data matches between a previous input data chunk and previous similar repository intervals. Digest segmentations of the similar repository intervals are projected onto the input data chunk, starting at the anchor positions.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application, listed as U.S. application Ser. No. 13/941,873,is cross-related to the following seventeen applications each listed as:U.S. application Ser. Nos. 13/941,703, 13/941,951, 13/941,782,13/941,886, 13/941,896, 13/941,694, 13/941,711, 13/941,958, 13/941,714,13/941,742, 13/941,769, 13/942,009, 13/941,982, 13/941,800, 13/941,999,13/942,027, and 13/942,048, all of which are filed on the same day asthe present invention, Jul. 15, 2013, and the entire contents of whichare incorporated herein by reference and are relied upon for claimingthe benefit of priority.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to computers, and moreparticularly to calculation of digest segmentations for input data usingsimilar data in a data deduplication system in a computing environment.

Description of the Related Art

In today's society, computer systems are commonplace. Computer systemsmay be found in the workplace, at home, or at school. Computer systemsmay include data storage systems, or disk storage systems, to processand store data. Large amounts of data have to be processed daily and thecurrent trend suggests that these amounts will continue beingever-increasing in the foreseeable future. An efficient way to alleviatethe problem is by using deduplication. The idea underlying adeduplication system is to exploit the fact that large parts of theavailable data are copied again and again, by locating repeated data andstoring only its first occurrence. Subsequent copies are replaced withpointers to the stored occurrence, which significantly reduces thestorage requirements if the data is indeed repetitive.

SUMMARY OF THE DESCRIBED EMBODIMENTS

In one embodiment, a method is provided for calculation of digestsegmentations for input data using similar data in a data deduplicationsystem using a processor device in a computing environment. In oneembodiment, by way of example only, a stream of input data ispartitioned into input data chunks. Similar repository intervals arecalculated for each input data chunk. Anchor positions are determinedbetween an input data chunk and the similar repository intervals, basedon data matches between a previous input data chunk and previous similarrepository intervals. Digest segmentations of the similar repositoryintervals are projected onto the input data chunk, starting at theanchor positions.

In another embodiment, a computer system is provided for calculation ofdigest segmentations for input data using similar data in a datadeduplication system using a processor device, in a computingenvironment. The computer system includes a computer-readable medium anda processor in operable communication with the computer-readable medium.In one embodiment, by way of example only, the processor, partitions astream of input data into input data chunks. Similar repositoryintervals are calculated for each input data chunk. Anchor positions aredetermined between an input data chunk and the similar repositoryintervals, based on data matches between a previous input data chunk andprevious similar repository intervals. Digest segmentations of thesimilar repository intervals are projected onto the input data chunk,starting at the anchor positions.

In a further embodiment, a computer program product is provided forcalculation of digest segmentations for input data using similar data ina data deduplication system using a processor device, in a computingenvironment. The computer-readable storage medium has computer-readableprogram code portions stored thereon. The computer-readable program codeportions include a first executable portion that, partitions a stream ofinput data into input data chunks. Similar repository intervals arecalculated for each input data chunk. Anchor positions are determinedbetween an input data chunk and the similar repository intervals, basedon data matches between a previous input data chunk and previous similarrepository intervals. Digest segmentations of the similar repositoryintervals are projected onto the input data chunk, starting at theanchor positions.

In addition to the foregoing exemplary method embodiment, otherexemplary system and computer product embodiments are provided andsupply related advantages. The foregoing summary has been provided tointroduce a selection of concepts in a simplified form that are furtherdescribed below in the Detailed Description. This Summary is notintended to identify key features or essential features of the claimedsubject matter, nor is it intended to be used as an aid in determiningthe scope of the claimed subject matter. The claimed subject matter isnot limited to implementations that solve any or all disadvantages notedin the background.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict embodiments of the invention and are not therefore to beconsidered to be limiting of its scope, the invention will be describedand explained with additional specificity and detail through the use ofthe accompanying drawings, in which:

FIG. 1 is a block diagram illustrating a computing system environmenthaving an example storage device in which aspects of the presentinvention may be realized;

FIG. 2 is a block diagram illustrating a hardware structure of datastorage system in a computer system in which aspects of the presentinvention may be realized;

FIG. 3 is a flowchart illustrating an exemplary method for digestretrieval based on similarity search in deduplication processing in adata deduplication system in which aspects of the present invention maybe realized;

FIG. 4 is a flowchart illustrating an exemplary alternative method fordigest retrieval based on similarity search in deduplication processingin a data deduplication system in which aspects of the present inventionmay be realized;

FIG. 5 is a flowchart illustrating an exemplary method for efficientcalculation of both similarity search values and boundaries of digestblocks using a single linear calculation of rolling hash values in adata deduplication system in which aspects of the present invention maybe realized;

FIG. 6 is a flowchart illustrating an exemplary method 600 fordeduplication processing of an input data chunk in a data deduplicationsystem in which aspects of the present invention may be realized; and

FIG. 7 is a flowchart illustrating an exemplary method for calculatingcandidate segmentations for an input data chunk in a data deduplicationsystem in which aspects of the present invention may be realized.

DETAILED DESCRIPTION OF THE DRAWINGS

Data deduplication is a highly important and vibrant field in computingstorage systems. Data deduplication refers to the reduction and/orelimination of redundant data. In data deduplication, a data object,which may be a file, a data stream, or some other form of data, isbroken down into one or more parts called chunks or blocks. In a datadeduplication process, duplicate copies of data are reduced oreliminated, leaving a minimal amount of redundant copies, or a singlecopy of the data, respectively. The goal of a data deduplication systemis to store a single copy of duplicated data, and the challenges inachieving this goal are efficiently finding the duplicate data patternsin a typically large repository, and storing the data patterns in astorage efficient deduplicated form. A significant challenge indeduplication storage systems is scaling to support very largerepositories of data. Such large repositories can reach sizes ofPetabytes (1 Petabyte=2⁵⁰ bytes) or more. Deduplication storage systemssupporting such repository sizes, must provide efficient processing forfinding duplicate data patterns within the repositories, whereefficiency is measured in resource consumption for achievingdeduplication (resources may be CPU cycles, RAM storage, persistentstorage, networking, etc.). In one embodiment, a deduplication storagesystem may be based on maintaining a search optimized index of valuesknown as fingerprints or digests, where a (small) fingerprint representsa (larger) block of data in the repository. The fingerprint values maybe cryptographic hash values calculated based on the blocks' data. Inone embodiment, secure hash algorithm (SHA), e.g. SHA-1 or SHA-256,which are a family of cryptographic hash functions, may be used.Identifying fingerprint matches, using index lookup, enables to storereferences to data that already exists in a repository. In oneembodiment, block boundaries may be determined based on the data itself.

To provide reasonable deduplication in this approach, the mean size ofthe data blocks based on which fingerprints are generated must belimited to smaller sizes and may not be too large. The reason being thata change of a bit within a data block will probabilistically change thedata block's corresponding fingerprint, and thus having large datablocks makes the scheme more sensitive to updates in the data ascompared to having small blocks. A typical data block size may rangefrom 4 KB to 64 KB, depending on the type of application and workload.Thus, by way of example only, small data blocks may range in sizes of upto 64 KB, and large data blocks are those data blocks having a sizelarger than 64 KB.

To support very large repositories scaling to Petabytes (e.g.,repositories scaling to at least one Petabyte), the number offingerprints to store coupled with the size of a fingerprint (rangingbetween 16 bytes and 64 bytes), becomes prohibitive. For example, for 1Petabyte of deduplicated data, with a 4 KB mean data block size, and 32bytes fingerprint size (e.g. of SHA-256), the storage required to storethe fingerprints is 8 Terabytes. Maintaining a search optimized datastructure for such volumes of fingerprints is difficult, and requiresoptimization techniques. However existing optimization techniques do notscale to these sizes while maintaining performance. For this reason, toprovide reasonable performance, the supported repositories have to berelatively small (on the order of tens of TB). Even for such smallersizes, considerable challenges and run-time costs arise due to the largescale of the fingerprint indexes, that create a bottle-neck (e.g., chunklook disk bottleneck) in deduplication processing.

To solve this problem, in one embodiment, a deduplication system may bebased on a two-step approach for searching data patterns duringdeduplication. In the first step, a large chunk of incoming data (e.g. afew megabytes) is searched in the repository for similar (rather thanidentical) data chunks of existing data, and the incoming data chunk ispartitioned accordingly into intervals and paired with corresponding(similar) repository intervals. In the second step, a byte-wise matchingalgorithm is applied on pairs of similar intervals, to identifyidentical sub-intervals, which are already stored in a repository ofdata. The matching algorithm of the second step relies on reading allthe relevant similar data in the repository in order to compare itbyte-wise to the input data.

Yet, a problem stemming from a byte-wise comparison of data underlyingthe matching algorithm of the second step, is that data of roughly thesame size and rate as the incoming data should be read from therepository, for comparison purposes. For example, a system processing 1GB of incoming data per second, should read about 1 GB of data persecond from the repository for byte-wise comparison. This requiressubstantially high capacities of I/O per second of the storage devicesstoring the repository data, which in turn increases their cost.

Additional trends in information technology coinciding with the aboveproblem are the following: (1) Improvements in the computing ability byincreasing CPU speeds and the number of CPU cores. (2) Increase in diskdensity, while disk throughput remains relatively constant or improvingonly modestly. This means that there are fewer spindles relative to thedata capacity, thus practically reducing the overall throughput. Due tothe problem specified above, there is a need to design an alternativesolution, to be integrated in a two step deduplication system embodimentspecified above, that does not require reading from the repository inhigh rates/volumes.

Therefore, in one embodiment, by way of example only, additionalembodiments address these problem, as well as shifts resourceconsumption from disks to the CPUs, to benefit from the above trends.The embodiments described herein are integrated within the two-step andscalable deduplication embodiments embodiment described above, and usesa similarity search to focus lookup of digests during deduplication. Inone embodiment, a global similarity search is used as a basis forfocusing the similarity search for digests of repository data that ismost likely to match input data.

The embodiments described herein significantly reduce the capacity ofI/O per second required of underlying disks, benefit from the increasesin computing ability and in disk density, and considerably reduce thecosts of processing, as well as maintenance costs and environmentaloverhead (e.g. power consumption).

In one embodiment, input data is segmented into small segments (e.g. 4KB) and a digest (a cryptographic hash value, e.g. SHA1) is calculatedfor each such segment. First, a similarity search algorithm, asdescribed above, is applied on an input chunk of data (e.g. 16 MB), andthe positions of the most similar reference data in the repository arelocated and found. These positions are then used to lookup the digestsof the similar reference data. The digests of all the data contained inthe repository are stored and retrieved in a form that corresponds totheir occurrence in the data. Given a position of a section of datacontained in the repository, the digests associated with the section ofdata are efficiently located in the repository and retrieved. Next,these reference digests are loaded into memory, and instead of comparingdata to find matches, the input digests and the loaded reference digestsare matched.

The described embodiments provide a new fundamental approach forarchitecting a data deduplication system, which integrates a scalabletwo step approach of similarity search followed by a search of identicalmatching segments, with an efficient and cost effectivedigest/fingerprint based matching algorithm (instead of byte-wise datacomparison). The digest/fingerprint based matching algorithm enables toread only a small fraction (1%) of the volume of data required bybyte-wise data comparison. The present invention proposed herein, adeduplication system can provide high scalability to very large datarepositories, in addition to high efficiency and performance, andreduced costs of processing and hardware.

In one embodiment, by way of example only, the term “similar data” maybe referred to as: for any given input data, data which is similar tothe input data is defined as data which is mostly the same (i.e. notentirely but at least 50% similar) as the input data. From looking atthe data in a binary view (perspective), this means that similar data isdata where most (i.e. not entirely but at least 50% similar) of thebytes are the same as the input data.

In one embodiment, by way of example only, the term “similar search” maybe referred to as the process of searching for data which is similar toinput data in a repository of data. In one embodiment, this process maybe performed using a search structure of similarity elements, which ismaintained and searched within.

In one embodiment, by way of example only, the term “similarityelements” may be calculated based on the data and facilitate a globalsearch for data which is similar to input data in a repository of data.In general, one or more similarity elements are calculated, andrepresent, a large (e.g. at least 16 MB) chunk of data.

Thus, the various embodiments described herein provide various solutionsfor digest retrieval based on a similarity search in deduplicationprocessing in a data deduplication system using a processor device in acomputing environment. In one embodiment, by way of example only, inputdata is partitioned into fixed sized data chunks. Similarity elements,digest block boundaries and digest values are calculated for each of thefixed sized data chunks. Matching similarity elements are searched forin a search structure (i.e. index) containing the similarity elementsfor each of the fixed sized data chunks in a repository of data.Positions of similar data are located in a repository. The positions ofthe similar data are used to locate and load into the memory storeddigest values and corresponding stored digest block boundaries of thesimilar data in the repository. The digest values and the correspondingdigest block boundaries are matched with the stored digest values andthe corresponding stored digest block boundaries to find data matches.

In one embodiment, the present invention provides a solution forutilizing a similarity search to load into memory the relevant digestsfrom the repository, for efficient deduplication processing. In a datadeduplication system, deduplication is performed by partitioning thedata into large fixed sized chunks, and for each chunk calculating (2things—similarity elements and digest blocks/digest values) hash values(digest block/digest value) for similarity search and digest values. Thedata deduplication system searches for matching similarity values of thechunks in a search structure of similarity values, and finds thepositions of similar data in the repository. The data deduplicationsystem uses these positions of similar data to locate and load intomemory stored digests of the similar repository data, and matching inputand repository digest values to find data matches.

In one embodiment, the present invention provides for efficientcalculation of both similarity search values and segmentation (i.e.boundaries) of digest blocks using a single linear calculation ofrolling hash values. In a data deduplication system, the input data ispartitioned into chunks, and for each chunk a set of rolling hash valuesis calculated. A single linear scan of the rolling hash values producesboth similarity search values and boundaries of the digest blocks of thechunk. Each rolling hash value corresponds to a consecutive window ofbytes in byte offsets. The similarity search values are used to searchfor similar data in the repository. The digest blocks segmentation isused to calculate digest block boundaries and corresponding digestvalues of the chunk, for digests matching. Each rolling hash valuecontributes to the calculation of the similarity values and to thecalculation of the digest blocks segmentations. Each rolling hash valuemay be discarded after contributing to the calculations. The describedembodiment provides significant processing efficiency and reduction ofCPU consumption, as well as considerable performance improvement.

Thus, as described above, the deduplication approach of the presentinvention uses a two-step process for searching data patterns duringdeduplication. In the first step, a large chunk of incoming data (e.g.16 megabytes “MB”) is searched in the repository for similar (ratherthan identical) chunks of existing data, and the incoming chunk ispartitioned accordingly into intervals, and paired with corresponding(similar) repository intervals. The similarity search structure (or“index”) used in the first step is compact and simple to maintain andsearch within, because the elements used for a similarity search arevery compact relative to the data they represent (e.g. 16 bytesrepresenting 4 megabytes). Further included in the first step, inaddition to a calculation of similarity elements, is a calculation ofdigest segments and respective digest values for the input chunk ofdata. All these calculations are based on a single calculation ofrolling hash values. In the second step, reference digests of thesimilar repository intervals are retrieved, and then the input digestsare matched with the reference digests, to identify data matches.

In one embodiment, in the similarity based deduplication approach asdescribed herein, a stream of input data is partitioned into chunks(e.g. at least 16 MB), and each chunk is processed in two main steps. Inthe first step a similarity search process is applied, and positions ofthe most similar reference data in the repository are found. Within thisstep both similarity search elements and digest segments boundaries arecalculated for the input chunk, based on a single linear calculation ofrolling hash values. Digest values are calculated for the input chunkbased on the produced segmentation, and stored in memory in the sequenceof their occurrence in the input data. The positions of similar data arethen used to lookup the digests of the similar reference data and loadthese digests into memory, also in a sequential form. Then, the inputdigests are matched with the reference digests to form data matches.

When deduplication of an input chunk of data is complete, the digestsassociated with the input chunk of data are stored in the repository, toserve as reference digests for subsequent input data. The digests arestored in a linear form, which is independent of the deduplicated formby which the data these digests describe is stored, and in the sequenceof their occurrence in the data. This method of storage enablesefficient retrieval of sections of digests, independent of fragmentationcharacterizing deduplicated storage forms, and thus low on IO andcomputational resource consumption.

While the above process is efficient, specific properties of backupenvironments can be leveraged to further improve the efficiency of thesolution. An important observation, which has been proven to be highlycharacteristic of backup environments, is that when an interval ofrepository data is identified as similar to a chunk of input data, thereis considerably high probability that the data following this intervalin the repository will be referenced shortly after by subsequent inputdata. This important property enables designing a solution where thesimilarity search step can be avoided as long as deduplication of theinput data, based on previously determined references, is producing goodresults. In one embodiment, by way of example only, one example of gooddeduplication results is coverage of the input chunk of data, withmatches to repository data, equal to and/or exceeding 70% of the size ofthe input chunk of data. This optimization saves considerable resourcesduring run-time (e.g. IO operations, CPU consumption, networkingconsumption, serialization), as well as provides a layer of protectionfrom possible spurious similarity search results (workloads which aremore difficult for deduplication may produce at times spurioussimilarity search results), thus also improving the deduplicationresults.

Furthermore, in cases where the similarity search step is avoided, thecalculation of the similarity elements is practically not required.Designing a way to also remove the need to calculate segmentation of theinput data to digest segments, will enable to entirely avoid thecalculation of the rolling hash values for chunks of input data. Sincecalculation of rolling hash values (i.e. a hash value for each seed ateach byte offset of the input data), is a computationally intenseoperation, avoiding it will result in considerable improvement in theefficiency of the deduplication process. Therefore, an additionalproblem in this context is how to calculate digest segments andrespective digest values for the input data, where similarity search isavoided.

The present invention provides a solution for both these problems. Inone embodiment, the present invention provides a first algorithmenabling to reduce the frequency of applying similarity search for inputdata, and then a second algorithm enables to calculate digest segmentsand respective digest values for input data, without calculating rollinghash values. The algorithms of the present invention provideconsiderable additional efficiency and effectiveness of thededuplication process.

In one embodiment of the present invention, a stream of input data ispartitioned into chunks (e.g. 16 MB), and the stream is assigned with adedicated data structure, denoted as “reference set”, which contains acurrent set of positions into the repository data, where each positionstarts an interval of repository data identified as similar to the lastchunk of data in the input stream. This data structure also contains ameasure of the goodness of the deduplication result of the last chunk ofdata in the input stream. At initiation the set of positions is empty,and the measure of deduplication result is set to nil.

In one embodiment, a deduplication process receives a chunk of inputdata, associated with a specific input stream, and determines whether toactivate similarity search for the input chunk, based on the informationin the reference set data structure. If the measure of goodness of thededuplication result is low (e.g. below a predetermined deduplicationresult threshold) or nil, then similarity search is activated, and itsresults, namely the positions associated with the set of repositoryintervals which are identified as similar, are inserted into thereference set, replacing previous contents of the reference set ifexists. Within the similarity search calculations, also digest segmentsboundaries and respective digest values are calculated for the inputchunk, based on the calculated rolling hash values. If, on the otherhand, the measure of goodness of the deduplication result issufficiently high (e.g., above a predetermined deduplication resultthreshold), also implying that the set of positions in the reference setis not empty, then similarity search is avoided, and the positions inthe reference set are updated, to reflect repository intervalsimmediately following the previous repository intervals.

In one embodiment, the positions of the similar repository dataintervals are then used to lookup their respective digests and loadthese digests into memory for matching. When deduplication processing ofan input chunk of data completes, the measure of goodness of thededuplication result is calculated for the input chunk, and updated inthe reference set data structure. If the measure of goodness of thededuplication result of the input chunk is low, and the input chunk wasprocessed without similarity search, then the input chunk is reprocessedwith similarity search.

In cases where similarity search is avoided, digest segments boundariesare calculated by determining appropriate anchor positions in the inputchunk and in the similar reference intervals, and projecting thesegmentations of the reference intervals on the input chunk. For eachprojected segmentation, respective digest values are calculated, andthen used for matching with the repository digests. Thus, the algorithmsof the present invention considerably increase the efficiency,throughput and effectiveness of the deduplication process.

In one embodiment, focusing on a conditional activation of similaritysearch for an input chunk based on the deduplication result of aprevious chunk in the input stream, a stream of input data ispartitioned into chunks, and a determination is made as to whether toapply similarity search for an input chunk based on the deduplicationresult of a previous chunk in the input stream. If the deduplicationresult of a previous chunk in the input stream is not sufficiently highor does not exist, then similarity search is applied. If thededuplication result of a previous chunk in the input stream issufficiently high then similarity search is avoided. In one embodiment,specifications of the similar intervals produced by similarity searchare stored in a reference set data structure associated with the inputstream, replacing any previous contents, if exists, in the reference setdata structure. The positions of the current similar intervals in therepository are calculated based on the positions of previous similarintervals in the repository, by incrementing the positions of theprevious similar intervals to reflect current similar intervalsimmediately following the previous similar intervals. The deduplicationresult of a previous chunk in the input stream is defined as the totalmatched size of the chunk divided by the total size of the chunk. Thetotal matched size of a chunk in the input stream is defined as thetotal size of the portions of the chunk, which are covered by matches torepository data. If the deduplication result of a current input chunk isnot sufficiently high after deduplication processing without similaritysearch, then the input chunk is reprocessed with similarity searchapplied.

In one embodiment, focusing on the calculation of candidate digestsegmentations for an input chunk, based on information of similarrepository intervals, and without calculating rolling hash values forthe input chunk, the present invention activates the calculationalgorithm in cases where similarity search is avoided, thus alsoavoiding the rolling hash calculation for the input chunk. In oneembodiment, a stream of input data is partitioned into chunks, andrepository intervals, which are similar to an input chunk, aredetermined. Then for each similar repository interval, its digestsegmentation is projected onto the input chunk starting at an anchorposition, thus forming a plurality of alternative digest segmentationsfor the input chunk. In one embodiment, an anchor position is defined asa pair of ending positions of a last data match, in the input data andin the repository data, calculated between a previous chunk in the sameinput stream and a previous similar repository interval, whose endingpositions in the input stream and in the repository data are closest tothe starting positions of the current input chunk and of the currentsimilar repository interval respectively. The position and size of thelast data match for each similar repository interval are stored for eachinput stream. The projection of a digest segmentation onto the inputchunk is done based on the positions and sizes of the digest segmentsfollowing an anchor position. Namely, the positions and sizes of thedigest segments of a similar repository interval, starting at an anchorposition, are projected onto the input chunk, starting at the respectiveanchor position. A candidate digest segmentation is calculated for theinput chunk based on each one of the similar intervals, and respectivedigest values are computed using each candidate segmentation. Thecandidate segmentation that produced the best deduplication ratio forthe input chunk is selected for storage. If the input chunk ispartitioned into sub-sections, such that each sub-section has its ownset of similar repository intervals, then the digest segmentationsselected for each sub-section are concatenated into a single digestsegmentation.

Turning now to FIG. 1, exemplary architecture 10 of a computing systemenvironment is depicted. The computer system 10 includes centralprocessing unit (CPU) 12, which is connected to communication port 18and memory device 16. The communication port 18 is in communication witha communication network 20. The communication network 20 and storagenetwork may be configured to be in communication with server (hosts) 24and storage systems, which may include storage devices 14. The storagesystems may include hard disk drive (HDD) devices, solid-state devices(SSD) etc., which may be configured in a redundant array of independentdisks (RAID). The operations as described below may be executed onstorage device(s) 14, located in system 10 or elsewhere and may havemultiple memory devices 16 working independently and/or in conjunctionwith other CPU devices 12. Memory device 16 may include such memory aselectrically erasable programmable read only memory (EEPROM) or a hostof related devices. Memory device 16 and storage devices 14 areconnected to CPU 12 via a signal-bearing medium. In addition, CPU 12 isconnected through communication port 18 to a communication network 20,having an attached plurality of additional computer host systems 24. Inaddition, memory device 16 and the CPU 12 may be embedded and includedin each component of the computing system 10. Each storage system mayalso include separate and/or distinct memory devices 16 and CPU 12 thatwork in conjunction or as a separate memory device 16 and/or CPU 12.

FIG. 2 is an exemplary block diagram 200 showing a hardware structure ofa data storage system in a computer system according to the presentinvention. Host computers 210, 220, 225, are shown, each acting as acentral processing unit for performing data processing as part of a datastorage system 200. The cluster hosts/nodes (physical or virtualdevices), 210, 220, and 225 may be one or more new physical devices orlogical devices to accomplish the purposes of the present invention inthe data storage system 200. In one embodiment, by way of example only,a data storage system 200 may be implemented as IBM® ProtecTIER®deduplication system TS7650G™. A Network connection 260 may be a fibrechannel fabric, a fibre channel point to point link, a fibre channelover ethernet fabric or point to point link, a FICON or ESCON I/Ointerface, any other I/O interface type, a wireless network, a wirednetwork, a LAN, a WAN, heterogeneous, homogeneous, public (i.e. theInternet), private, or any combination thereof. The hosts, 210, 220, and225 may be local or distributed among one or more locations and may beequipped with any type of fabric (or fabric channel) (not shown in FIG.2) or network adapter 260 to the storage controller 240, such as Fibrechannel, FICON, ESCON, Ethernet, fiber optic, wireless, or coaxialadapters. Data storage system 200 is accordingly equipped with asuitable fabric (not shown in FIG. 2) or network adaptor 260 tocommunicate. Data storage system 200 is depicted in FIG. 2 comprisingstorage controllers 240 and cluster hosts 210, 220, and 225. The clusterhosts 210, 220, and 225 may include cluster nodes.

To facilitate a clearer understanding of the methods described herein,storage controller 240 is shown in FIG. 2 as a single processing unit,including a microprocessor 242, system memory 243 and nonvolatilestorage (“NVS”) 216. It is noted that in some embodiments, storagecontroller 240 is comprised of multiple processing units, each withtheir own processor complex and system memory, and interconnected by adedicated network within data storage system 200. Storage 230 (labeledas 230 a, 230 b, and 230 n in FIG. 3) may be comprised of one or morestorage devices, such as storage arrays, which are connected to storagecontroller 240 (by a storage network) with one or more cluster hosts210, 220, and 225 connected to each storage controller 240.

In some embodiments, the devices included in storage 230 may beconnected in a loop architecture. Storage controller 240 manages storage230 and facilitates the processing of write and read requests intendedfor storage 230. The system memory 243 of storage controller 240 storesprogram instructions and data, which the processor 242 may access forexecuting functions and method steps of the present invention forexecuting and managing storage 230 as described herein. In oneembodiment, system memory 243 includes, is in association with, or is incommunication with the operation software 250 for performing methods andoperations described herein. As shown in FIG. 2, system memory 243 mayalso include or be in communication with a cache 245 for storage 230,also referred to herein as a “cache memory”, for buffering “write data”and “read data”, which respectively refer to write/read requests andtheir associated data. In one embodiment, cache 245 is allocated in adevice external to system memory 243, yet remains accessible bymicroprocessor 242 and may serve to provide additional security againstdata loss, in addition to carrying out the operations as described inherein.

In some embodiments, cache 245 is implemented with a volatile memory andnon-volatile memory and coupled to microprocessor 242 via a local bus(not shown in FIG. 2) for enhanced performance of data storage system200. The NVS 216 included in data storage controller is accessible bymicroprocessor 242 and serves to provide additional support foroperations and execution of the present invention as described in otherfigures. The NVS 216, may also referred to as a “persistent” cache, or“cache memory” and is implemented with nonvolatile memory that may ormay not utilize external power to retain data stored therein. The NVSmay be stored in and with the cache 245 for any purposes suited toaccomplish the objectives of the present invention. In some embodiments,a backup power source (not shown in FIG. 2), such as a battery, suppliesNVS 216 with sufficient power to retain the data stored therein in caseof power loss to data storage system 200. In certain embodiments, thecapacity of NVS 216 is less than or equal to the total capacity of cache245.

Storage 230 may be physically comprised of one or more storage devices,such as storage arrays. A storage array is a logical grouping ofindividual storage devices, such as a hard disk. In certain embodiments,storage 230 is comprised of a JBOD (Just a Bunch of Disks) array or aRAID (Redundant Array of Independent Disks) array. A collection ofphysical storage arrays may be further combined to form a rank, whichdissociates the physical storage from the logical configuration. Thestorage space in a rank may be allocated into logical volumes, whichdefine the storage location specified in a write/read request.

In one embodiment, by way of example only, the storage system as shownin FIG. 2 may include a logical volume, or simply “volume,” may havedifferent kinds of allocations. Storage 230 a, 230 b and 230 n are shownas ranks in data storage system 200, and are referred to herein as rank230 a, 230 b and 230 n. Ranks may be local to data storage system 200,or may be located at a physically remote location. In other words, alocal storage controller may connect with a remote storage controllerand manage storage at the remote location. Rank 230 a is shownconfigured with two entire volumes, 234 and 236, as well as one partialvolume 232 a. Rank 230 b is shown with another partial volume 232 b.Thus volume 232 is allocated across ranks 230 a and 230 b. Rank 230 n isshown as being fully allocated to volume 238—that is, rank 230 n refersto the entire physical storage for volume 238. From the above examples,it will be appreciated that a rank may be configured to include one ormore partial and/or entire volumes. Volumes and ranks may further bedivided into so-called “tracks,” which represent a fixed block ofstorage. A track is therefore associated with a given volume and may begiven a given rank.

The storage controller 240 may include a data duplication module 255, asimilarity index module 257 (e.g., a similarity search structure), asimilarity search module 259, and a data structure module 260 (e.g., areference set data structure). The data duplication module 255, thesimilarity index module 257, the similarity search module 259, and thedata structure module 260 may work in conjunction with each and everycomponent of the storage controller 240, the hosts 210, 220, 225, andstorage devices 230. The data duplication module 255, the similarityindex module 257, the similarity search module 259, and the datastructure module 260 may be structurally one complete module or may beassociated and/or included with other individual modules. The dataduplication module 255, the similarity index module 257, the similaritysearch module 259, and the data structure module 260 may also be locatedin the cache 245 or other components.

The storage controller 240 includes a control switch 241 for controllingthe fiber channel protocol to the host computers 210, 220, 225, amicroprocessor 242 for controlling all the storage controller 240, anonvolatile control memory 243 for storing a microprogram (operationsoftware) 250 for controlling the operation of storage controller 240,data for control, cache 245 for temporarily storing (buffering) data,and buffers 244 for assisting the cache 245 to read and write data, acontrol switch 241 for controlling a protocol to control data transferto or from the storage devices 230, the data duplication module 255, thesimilarity index module 257, and the similarity search module 259, inwhich information may be set. Multiple buffers 244 may be implementedwith the present invention to assist with the operations as describedherein. In one embodiment, the cluster hosts/nodes, 210, 220, 225 andthe storage controller 240 are connected through a network adaptor (thiscould be a fibre channel) 260 as an interface i.e., via at least oneswitch called “fabric.”

In one embodiment, the host computers or one or more physical or virtualdevices, 210, 220, 225 and the storage controller 240 are connectedthrough a network (this could be a fibre channel) 260 as an interfacei.e., via at least one switch called “fabric.” In one embodiment, theoperation of the system shown in FIG. 2 will be described. Themicroprocessor 242 may control the memory 243 to store commandinformation from the host device (physical or virtual) 210 andinformation for identifying the host device (physical or virtual) 210.The control switch 241, the buffers 244, the cache 245, the operatingsoftware 250, the microprocessor 242, memory 243, NVS 216, dataduplication module 255, the similarity index module 257, the similaritysearch module 259, and the data structure module 260 are incommunication with each other and may be separate or one individualcomponent(s). Also, several, if not all of the components, such as theoperation software 250 may be included with the memory 243. Each of thecomponents within the devices shown may be linked together and may be incommunication with each other for purposes suited to the presentinvention. As mentioned above, the data duplication module 255, thesimilarity index module 257, the similarity search module 259, and thedata structure module 260 may also be located in the cache 245 or othercomponents. As such, the data duplication module 255, the similarityindex module 257, the similarity search module 259, and the datastructure module 260 maybe used as needed, based upon the storagearchitecture and users preferences.

As mentioned above, in one embodiment, the input data is partitionedinto large fixed size chunks (e.g. 16 MB), and a similarity searchprocedure is applied for each input chunk. A similarity search procedurecalculates compact similarity elements, which may also be referred to asdistinguishing characteristics (DCs), based on the input chunk of data,and searches for matching similarity elements stored in a compact searchstructure (i.e. index) in the repository. The size of the similarityelements stored per each chunk of data is typically 32 bytes (where thechunk size is a few megabytes), thus making the search structure storingthe similarity elements very compact and simple to maintain and searchwithin.

The similarity elements are calculated by calculating rolling hashvalues on the chunk's data, namely producing a rolling hash value foreach consecutive window of bytes in a byte offset, and then selectingspecific hash values and associated positions (not necessarily the exactpositions of these hash values) to be the similarity elements of thechunk.

One important aspect and novelty provided by the present invention isthat a single linear calculation of rolling hash values, which is acomputationally expensive operation, serves as basis for calculatingboth the similarity elements of a chunk (for a similarity search) andthe segmentation of the chunk's data into digest blocks (for findingexact matches). Each rolling hash value is added to the calculation ofthe similarity elements as well as to the calculation of the digestblocks segmentation. After being added to the two calculations, arolling hash value can be discarded, as the need to store the rollinghash values is minimized or eliminated. This algorithmic elementprovides significant efficiency and reduction of CPU consumption, aswell as considerable performance improvement.

In one embodiment, the similarity search procedure of the presentinvention produces two types of output. The first type of output is aset of positions of the most similar reference data in the repository.The second type of output is the digests of the input chunk, comprisingof the segmentation to digest blocks and the digest values correspondingto the digest blocks, where the digest values are calculated based onthe data of the digest blocks.

In one embodiment, the digests are stored in the repository in a formthat corresponds to the digests occurrence in the data. Given a positionin the repository and size of a section of data, the location in therepository of the digests corresponding to that interval of data isefficiently determined. The positions produced by the similarity searchprocedure are then used to lookup the stored digests of the similarreference data, and to load these reference digests into memory. Then,rather than comparing data, the input digests and the loaded referencedigests are matched. The matching process is performed by loading thereference digests into a compact search structure of digests in memory,and then for each input digest, querying the search structure of digestsfor existence of that digest value. Search in the search structure ofdigests is performed based on the digest values. If a match is found,then the input data associated with that digest is determined to befound in the repository and the position of the input data in therepository is determined based on the reference digest's position in therepository. In this case, the identity between the input data covered bythe input digest, and the repository data covered by the matchingreference digest, is recorded. If a match is not found then the inputdata associated with that digest is determined to be not found in therepository, and is recorded as new data. In one embodiment, thesimilarity search structure is a global search structure of similarityelements, and a memory search structure of digests' is a local searchstructure of digests in memory. The search in the memory searchstructure of digests is performed by the digest values.

FIG. 3 is a flowchart illustrating an exemplary method 300 for digestretrieval based on similarity search in deduplication processing in adata deduplication system in which aspects of the present invention maybe realized. The method 300 begins (step 302). The method 300 partitionsinput data into data chunks (step 304). The input data may bepartitioned into fixed sized data chunks. The method 300 calculates, foreach of the data chunks, similarity elements, digest block boundaries,and corresponding digest values are calculated (step 306). The method300 searches for matching similarity elements in a search structure(i.e. index) for each of the data chunks (which may be fixed size datachunks) (step 308). The positions of the similar data in a repository(e.g., a repository of data) are located (step 310). The method 300 usesthe positions of the similar data to locate and load into memory storeddigest values and corresponding stored digest block boundaries of thesimilar data in the repository (step 312). The method 300 matches thedigest values and the corresponding digest block boundaries of the inputdata with the stored digest values and the corresponding stored digestblock boundaries to find data matches (step 314). The method 300 ends(step 316).

FIG. 4 is a flowchart illustrating an exemplary alternative method 400for digest retrieval based on similarity search in deduplicationprocessing in a data deduplication system in which aspects of thepresent invention may be realized. The method 400 begins (step 402). Themethod 400 partitions the input data into chunks (e.g., partitions theinput data into large fixed size chunks) (step 404), and for an inputdata chunk calculates rolling hash values, similarity elements, digestblock boundaries, and digest values based on data of the input datachunk (step 406). The method 400 searches for similarity elements of theinput data chunk in a similarity search structure (i.e. index) (step 408and 410). The method 400 determines if there are enough or a sufficientamount of matching similarity elements (step 412). If not enoughmatching similarity elements are found then the method 400 determinesthat no similar data is found in the repository for the input datachunk, and the data of the input chunk is stored in a repository (step414) and then the method 400 ends (step 438). If enough similarityelements are found, then for each similar data interval found in arepository, the method 400 determines the position and size of eachsimilar data interval in the repository (step 416). The method 400locates the digests representing the similar data interval in therepository (step 418). The method 400 loads these digests into a searchdata structure of digests in memory (step 420). The method 400determines if there are any additional similar data intervals (step422). If yes, the method 400 returns to step 416. If no, the method 400considers each digest of the input data chunk (step 424). The method 400determines if the digest value exists in the memory search structure ofdigests (step 426). If yes, the method 400 records the identity betweenthe input data covered by the digest and the repository data having thematching digest value (step 428). If no, the method 400 records that theinput data covered by the digest is not found in the repository (step430). From both steps 428 and 430, the method 400 determines if thereare additional digests of the input data chunk (step 432). If yes, themethod 400 returns to step 424. If no, method 400 removes the similarityelements of the matched data in the repository from the similaritysearch structure (step 434 and step 410). The method 400 adds thesimilarity elements of the input data chunk to the similarity searchstructure (step 436). The method 400 ends (step 438).

FIG. 5 is a flowchart illustrating an exemplary method 500 for efficientcalculation of both similarity search values and boundaries of digestblocks using a single linear calculation of rolling hash values in adata deduplication system in which aspects of the present invention maybe realized. The method 500 begins (step 502). The method 500 partitionsinput data into data chunks (steps 504). The data chunks may be fixedsized data chunks. The method 500 considers each consecutive window ofbytes in a byte offset in the input data (step 506). The method 500determines if there is an additional consecutive window of bytes to beprocessed (step 508). If yes, the method 500 calculates a rolling hashvalue based on the data of the consecutive window of bytes (step 510).The method 500 contributes the rolling hash value to the calculation ofthe similarity values and to the calculation of the digest blockssegmentations (i.e., the digest block boundaries) (step 512). The method500 discards the rolling hash value (step 514), and returns to step 506.If no, the method 500 concludes the calculation of the similarityelements and of the digest blocks segmentation, producing the finalsimilarity elements and digest blocks segmentation of the input data(step 516). The method 500 calculates digest values based on the digestblocks segmentation, wherein each digest block is assigned with acorresponding digest value (step 518). The similarity elements are usedto search for similar data in the repository (step 520). The digestblocks and corresponding digest values are used for matching with digestblocks and corresponding digest values stored in a repository fordetermining data in the repository which is identical to the input data(step 522). The method 500 ends (step 524).

As mentioned above, in one embodiment, each input stream of data isassigned with a dedicated data structure, denoted as a “reference set”,which contains a current set of positions into the repository data,where each position starts an interval of repository data identified assimilar to the last chunk of data in the input stream. This datastructure also contains a measure of the goodness of the deduplicationresult of the previous chunk of data in the input stream, which isdefined as the deduplication ratio of the previous chunk, namely thetotal size of the portions of the previous chunk which are covered bymatches to repository data divided by the total size of the previouschunk. In one embodiment, the deduplication ratio value is defined assufficiently good if it is not less than a predefined threshold. Anexample of a predefined threshold would be 70%. If the deduplicationratio value is less than the predefined threshold then the deduplicationratio value is defined as not sufficiently good (e.g., less than thepredefined threshold).

A deduplication process receives a chunk of input data, associated witha specific input stream, and determines whether to activate similaritysearch for the input chunk, based on the information in the referenceset data structure. In one embodiment, two states are defined asfollows: A “reference set recalculation state” is applied at thebeginning of an input stream, and in cases where deduplicationprocessing of an input chunk did not yield a sufficiently gooddeduplication result. This state triggers activation of similaritysearch for an input chunk. A “valid reference set state” is applied whenprocessing an input chunk which is not at the beginning of an inputstream, and where deduplication processing of the previous chunk in theinput stream provided a sufficiently good deduplication result. In thisstate, similarity search is avoided. The state changes from referenceset recalculation to valid reference set when the deduplication resultof an input chunk is sufficiently good; and changes back to referenceset recalculation when the deduplication result of an input chunk is notsufficiently good.

As described in FIG. 6, below, the method of the present inventionreceives as input an input chunk of data associated with a specificstream of input data, and a reference set data structure associated withthe input stream. If the measure of deduplication result in thereference set is not sufficiently high or nil, then the method performsthe following: activates a similarity search for the input chunk, andobtains a list of similar repository intervals, where each interval isspecified by a position and size; and stores the specifications of thesimilar intervals in the reference set data structure, replacing anyprevious contents in the reference set data structure if exists. Withinsimilarity search, the following are calculated for the input chunk(based on calculation of rolling hash values): similarity elements,digest segments boundaries and respective digests values.

If the measure of deduplication result in the reference set issufficiently high, the method advances the positions in the referenceset data structure to reflect repository intervals immediately followingthe current repository intervals, and calculates digest segmentsboundaries and respective digest values for the input chunk based oninformation of the calculated similar repository intervals. The methodto achieve this is elaborated in the following.

In one embodiment, the similar intervals specified in the reference setdata structure are scanned and their associated digests are loaded fromthe repository into memory. The digests of separate repository intervalsare read in parallel. The method of the present invention then matchesthe input and the repository digests to find data matches, andcalculates a measure of goodness of the deduplication result of thecurrent input chunk. The measure is defined as the deduplication ratioof the chunk, which is the total size of the portions of the chunk thatare matched with repository data divided by the total size of the chunk.The measure value is stored in the reference set data structure. If themeasure of goodness of the deduplication result is not sufficiently highand the input chunk was processed without similarity search, then themethod of the present invention reprocesses the input chunk withapplication of similarity search.

If the measure of goodness of the deduplication result is sufficientlyhigh or the input chunk was processed with similarity search, then themethod of the present invention stores the digest segments boundariesand respective digest values of the input chunk in the repository, andalso stores the data of the input chunk in the repository in adeduplicated form, using the data matches and mismatches calculated forthe input chunk. Specifically, mismatched input data is stored, andmatched input data is recorded as references to matched repository data.

FIG. 6 is a flowchart illustrating an exemplary method 600 fordeduplication processing of an input data chunk in a data deduplicationsystem in which aspects of the present invention may be realized. Inother words, FIG. 6 is flowchart illustrating an exemplary method 600for deduplication processing of an input chunk with conditionalactivation of similarity search. The method 600 begins (step 602). Themethod 600 assigns each input stream of data with a dedicated datastructure, denoted as a “reference set,” which contains a current set ofpositions into the repository data, where each position starts aninterval of repository data identified as similar to the last chunk ofdata in the input stream, and also contains a measure of the goodness ofthe deduplication result of the last chunk of data in the input stream.The method 600 receives as input an input chunk of data associated witha specific stream of input data, and a reference set data structureassociated with the input stream (step 604). The method 600 determinesif the measure value of deduplication result in the reference set datastructure is not sufficiently high (e.g., below a predetermineddeduplication result threshold) or nil (step 606). An example value of apredetermined threshold for the deduplication result is 70%. If no, themethod 600 advances the positions in the reference set data structure toreflect repository intervals immediately following the currentrepository intervals (step 608). The method 600 then calculates digestsegments boundaries and respective digest values for the input chunkbased on information of the calculated similar repository intervals(step 610). Returning to step 606, if yes, the method 600 activates asimilarity search for the input data chunk and obtains a list of similarrepository intervals (step 612). Each interval is specified by aposition and a size. The method 600 then stores the specifications ofthe similar intervals in the reference set data structure and replacesany previous contents, if existing, in the reference set data structure(step 614). The method 600 calculates for the input chunk, within thesimilarity search, based on calculated rolling hash values, similarityelements, digest segments boundaries, and respective digests values(step 616). From both step 616 and 610, the method 600 then scans thesimilar intervals specified in the reference set data structure, andloads the digests associated with the similar intervals from therepository into a memory, and reads the digests of separate repositoryintervals in parallel (step 618). The method 600 matches the input andthe repository digests to find data matches (step 620). The method 600calculates a measure value of the goodness of the deduplication resultof the current input chunk, and stores the measure value in thereference set data structure (step 622). This measure is defined, in oneembodiment, as the deduplication ratio of the chunk, which is the totalsize of the portions of the chunk that are covered by matches torepository data divided by the total size of the chunk. The method 600then determines if the following conditions apply: the measure ofgoodness of the deduplication results is not sufficiently high and theinput data chunk was processed without similarity search (step 624). Ifyes, the method 600 returns to step 612. If no, the method 600 storesthe digest segments boundaries and respective digest values of the inputdata chunk in the repository (step 626). The method 600 stores the dataof the input chunk in the repository in a deduplicated form, using thedata matches and mismatches calculated for the input chunk. Mismatchedinput data is stored, and matched input data is recorded as referencesto matched repository data (step 628). The method 600 ends (step 630).

A further problem which should be solved is how to calculate digestsegments boundaries and respective digest values for an input chunkbased on information of the identified similar repository intervals, andwithout calculating rolling hash values for the input chunk (rollinghash values normally serve as basis for calculating digest segments,which then enable to calculate respective digest values). A method tosolve this problem is required for step 610 (see FIG. 6 step 610) in thealgorithm specified above, and will be activated in cases wheresimilarity search is avoided, thus also avoiding the rolling hashcalculation for the input chunk.

In one embodiment, the present invention provides an algorithm to solvethis problem. Turning now to FIG. 7 a block diagram illustrating anexemplary method 700 for calculating candidate segmentations for aninput data chunk in a data deduplication system in which aspects of thepresent invention may be realized, is illustrated. For each similarrepository interval 718B and 718D, an anchor position 714 (illustratedin FIG. 7 with 714A-B) is identified based on the information of datamatches 710 (illustrated in FIG. 7 with 710A-D) previously calculated.In FIG. 7, the two rectangles at the left side of the bottom tips oflines 714A-B are marked as 710C and 710D. These are the portions of theinput data covered by the matches whose repository portions are markedwith 710A and 710B. An anchor position 714 is defined, in oneembodiment, as a pair of ending positions of a data match in the inputdata and in the repository data 714A-B, calculated between a previouschunk in the same input stream 706 and a previous similar repositoryinterval 718A and 718C, whose ending positions in the input stream andin the repository data 714A-B are closest to the starting positions ofthe input chunk 704 and of the similar repository interval 718B and 718Drespectively. The 3 vertical lines 730, 740, and 750 partition in halfthe 3 horizontal lines 708A-C), where the horizontal lines 708 representdata intervals, the bottom line 750 is the input data and the top twolines (730 and 740) are similar repository data. The vertical lines 730,750 partition the data intervals to the current input chunk and similardata—on the right side, and the previous input chunk and similar data—onthe left side.

The specifications (i.e. position and size) of the last data match710A-D for each similar repository interval 718B and 718D are stored ina reference set data structure for each similar repository interval,upon completion of deduplication of each input chunk 704. Thisinformation is then available for usage by this algorithm for the nextinput chunk, in cases where similarity search is avoided. For eachsimilar repository interval 718B and 718D, an anchor position 714A and714B is identified as specified above, and then the digest segmentationof the repository data 712A and 712B starting at the anchor position714A and 714B is projected onto the input chunk 704 to form asegmentation on the input chunk 720A and 720B. Projection is done basedon the positions and sizes of the digest segments 712A and 712Bfollowing an anchor position 714A and 714B. Therefore, for each similarinterval 718B and 718D, a candidate digest segmentation 720A and 720B iscalculated for the input chunk 704, and respective digest values arecomputed. In the next step, the digests of the candidate segmentations720A and 720B calculated for the input chunk 704 are matched with thedigests of the similar repository intervals 718B and 718D, and datamatches are calculated (step 620 in FIG. 6).

In one embodiment of the present invention, in the algorithm ofcalculating candidate segmentations 720, all the candidate segmentationsof the input chunk 704 are essentially equivalent in their importance.So an arising problem is which segmentation of the input chunk should bestored, to serve as basis for deduplication of subsequent input chunks.In one embodiment, in the algorithm of the present invention, thecandidate segmentation 720 that produced the most comprehensive coverageof the input chunk 704 with matches (i.e. produced the bestdeduplication ratio) is selected for storage. If the input chunk 704 ispartitioned into sub-sections, such that each sub-section has its ownset of similar repository intervals 718B and 718D, then thesegmentations selected for each sub-section are concatenated into asingle segmentation.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that may contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wired, optical fiber cable, RF, etc., or any suitable combination of theforegoing. Computer program code for carrying out operations for aspectsof the present invention may be written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Java, Smalltalk, C++ or the like and conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages. The program code may execute entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention have been described above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, may beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that may direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks. The computer program instructions may also beloaded onto a computer, other programmable data processing apparatus, orother devices to cause a series of operational steps to be performed onthe computer, other programmable apparatus or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the above figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, may be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A method for calculation of digest segmentationsfor input data in a data deduplication system using a processor devicein a computing environment, comprising: partitioning an input stream ofdata into input data chunks, the input data chunks each being at least16 Megabytes (MB) in size; calculating similar repository intervals foran input data chunk, the repository intervals produced using a singlelinear scan of rolling hash values to calculate both similarity elementsand digest block boundaries corresponding to the repository intervals;wherein each of the rolling hash values are discarded upon contributingto the calculation; identifying anchor positions for the input datachunk and each one of the similar repository intervals; projectingdigest segmentations of the similar repository intervals starting at theanchor positions onto the input data chunk; wherein an anchor positionis defined as a pair of ending position of a last data match, in theinput data and in repository data, calculated between a previous inputdata chunk in the input stream of data and a previous similar repositoryinterval, whose ending positions in the input stream of data and in therepository data are closest to starting positions of a current inputdata chunk and of a current similar repository interval respectively;determining when and when not to apply a similarity search associatedwith the similar repository intervals for the input data chunk based ona deduplication result of a previous input data chunk in the inputstream of data; and avoiding the similarity search if the deduplicationresult of the previous input data chunk in the input stream of data isone of above and equal to a predetermined deduplication resultthreshold, thereby only calculating the rolling hash values of the inputdata chunk when needed to be used in the similarity search in the datadeduplication system of the computing environment.
 2. The method ofclaim 1, further including storing a position and a size of a last datamatch for each one of the similar repository intervals for each ofplurality of input streams of data.
 3. The method of claim 1, furtherincluding using positions and sizes of digest segments of a similarrepository interval starting at an anchor position in the similarrepository interval for projecting the positions and the sizes of thedigest segments onto the input data chunk starting from a respectiveanchor position in the input data chunk.
 4. The method of claim 1,further including calculating a plurality of digest segmentations forthe input data chunk using each one of the digest segmentations of thesimilar repository intervals, wherein respective digest values arecomputed for each one of the plurality of calculated digestsegmentation.
 5. The method of claim 4, further including selecting forstorage at least one digest segmentation of the plurality of digestsegmentations calculated for the input data chunk that produced ahighest deduplication ratio for the input data chunk.
 6. The method ofclaim 5, further including concatenating into a single segmentation theplurality of digest segmentations selected for all sub-sections of theinput data chunk if the input data chunk is partitioned intosub-sections such that each of the sub-sections has a set of the similarrepository intervals.
 7. A system for calculation of candidate digestsegmentations for an input data chunk in a data deduplication system ofa computing environment, the system comprising: the data deduplicationsystem; a repository operating in the data deduplication system; amemory in the data deduplication system; a search structure inassociation with the memory in the data deduplication system; and atleast one processor device operable in the computing storage environmentfor controlling the data deduplication system, wherein the at least oneprocessor device: partitions an input stream of data into input datachunks, the input data chunks each being at least 16 Megabytes (MB) insize, calculates similar repository intervals for an input data chunk,the repository intervals produced using a single linear scan of rollinghash values to calculate both similarity elements and digest blockboundaries corresponding to the repository intervals; wherein each ofthe rolling hash values are discarded upon contributing to thecalculation, identifies anchor positions for the input data chunk andeach one of the similar repository intervals, projects digestsegmentations of the similar repository intervals starting at the anchorpositions onto the input data chunk; wherein an anchor position isdefined as a pair of ending position of a last data match, in the inputdata and in repository data, calculated between a previous input datachunk in the input stream of data and a previous similar repositoryinterval, whose ending positions in the input stream of data and in therepository data are closest to starting positions of a current inputdata chunk and of a current similar repository interval respectively,determining when and when not to apply a similarity search associatedwith the similar repository intervals for the input data chunk based ona deduplication result of a previous input data chunk in the inputstream of data, and avoiding the similarity search if the deduplicationresult of the previous input data chunk in the input stream of data isone of above and equal to a predetermined deduplication resultthreshold, thereby only calculating the rolling hash values of the inputdata chunk when needed to be used in the similarity search in the datadeduplication system of the computing environment.
 8. The system ofclaim 7, wherein the at least one processor device stores a position anda size of a last data match for each one of the similar repositoryintervals for each of plurality of input streams of data.
 9. The systemof claim 7, wherein the at least one processor device uses positions andsizes of digest segments of a similar repository interval starting at ananchor position in the similar repository interval for projecting thepositions and the sizes of the digest segments onto the input data chunkstarting from a respective anchor position in the input data chunk. 10.The system of claim 7, wherein the at least one processor devicecalculates a plurality of digest segmentations for the input data chunkusing each one of the digest segmentations of the similar repositoryintervals, wherein respective digest values are computed for each one ofthe plurality of calculated digest segmentation.
 11. The system of claim10, wherein the at least one processor device selects for storage atleast one digest segmentation of the plurality of digest segmentationscalculated for the input data chunk that produced a highestdeduplication ratio for the input data chunk.
 12. The system of claim11, wherein the at least one processor device concatenates into a singlesegmentation the plurality of digest segmentations selected for allsub-sections of the input data chunk if the input data chunk ispartitioned into sub-sections such that each of the sub-sections has aset of the similar repository intervals.
 13. A computer program productfor calculation of candidate digest segmentations for an input datachunk in a data deduplication system using a processor device in acomputing environment, the computer program product comprising anon-transitory computer-readable storage medium having computer-readableprogram code portions stored therein, the computer-readable program codeportions comprising: a first executable portion that partitions an inputstream of data into input data chunks, the input data chunks each beingat least 16 Megabytes (MB) in size; a second executable portion thatcalculates similar repository intervals for an input data chunk, therepository intervals produced using a single linear scan of rolling hashvalues to calculate both similarity elements and digest block boundariescorresponding to the repository intervals; wherein each of the rollinghash values are discarded upon contributing to the calculation; a thirdexecutable portion that identifies anchor positions for the input datachunk and each one of the similar repository intervals; a fourthexecutable portion that projects digest segmentations of the similarrepository intervals starting at the anchor positions onto the inputdata chunk; wherein an anchor position is defined as a pair of endingposition of a last data match, in the input data and in repository data,calculated between a previous input data chunk in the input stream ofdata and a previous similar repository interval, whose ending positionsin the input stream of data and in the repository data are closest tostarting positions of a current input data chunk and of a currentsimilar repository interval respectively; a fifth executable portionthat determines when and when not to apply a similarity searchassociated with the similar repository intervals for the input datachunk based on a deduplication result of a previous input data chunk inthe input stream of data; and a sixth executable portion that avoids thesimilarity search if the deduplication result of the previous input datachunk in the input stream of data is one of above and equal to apredetermined deduplication result threshold, thereby only calculatingthe rolling hash values of the input data chunk when needed to be usedin the similarity search in the data deduplication system of thecomputing environment.
 14. The computer program product of claim 13,further including a seventh executable portion that stores a positionand a size of a last data match for each one of the similar repositoryintervals for each of plurality of input streams of data.
 15. Thecomputer program product of claim 13, further including a seventhexecutable portion that uses positions and sizes of digest segments of asimilar repository interval starting at an anchor position in thesimilar repository interval for projecting the positions and the sizesof the digest segments onto the input data chunk starting from arespective anchor position in the input data chunk.
 16. The computerprogram product of claim 13, further including a seventh executableportion that calculates a plurality of digest segmentations for theinput data chunk using each one of the digest segmentations of thesimilar repository intervals, wherein respective digest values arecomputed for each one of the plurality of calculated digestsegmentation.
 17. The computer program product of claim 16, furtherincluding an eighth executable portion that selects for storage at leastone digest segmentation of the plurality of digest segmentationscalculated for the input data chunk that produced a highestdeduplication ratio for the input data chunk.
 18. The computer programproduct of claim 17, further including a ninth executable portion thatconcatenates into a single segmentation the plurality of digestsegmentations selected for all sub-sections of the input data chunk ifthe input data chunk is partitioned into sub-sections such that each ofthe sub-sections has a set of the similar repository intervals.